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DFBDehazeNet: an end-to-end dense feedback network for single image dehazing

JOURNAL OF ELECTRONIC IMAGING 2021年第3期30卷 033004-033004页

作者： Guo, Mengyan Huang, Bo Zhang, Juan Wang, Feng Zhang, Yan Fang, Zhijun Shanghai Univ Engn Sci Sch Elect & Elect Engn Control Engn Shanghai Peoples R China

The feedback mechanism method of simulating the biological vision system has not been widely used in deep learning dehazing algorithms. To alleviate the difficulty of feature interaction, we combine the feedback mechanism with dense skip connections to fuse features of different levels in a dehazing network. Inspired by the feedback network in which previous network layers can have access to rich information processed by the following network layers, we propose an end-to-end dense feedback network (DFBDehazeNet) for single image dehazing that implements the feedback mechanism using hidden states of constrained RNN. The low-level hazy feature information can be continuously corrected by the high-level feature information obtained from the dense feedback block via the recurrent feedback connection. The top-down feedback mechanism is adopted in DFBDehazeNet to refine the low-level hazy feature information, thereby achieving a powerful image restoration effect. The ablation experiment proves that the iterative structure of DFBDehazeNet and the projection unit play an important role in removing haze from images. The experimental results show that the results of image haze removal are superior to the great majority of existing methods both qualitatively and quantitatively. (c) 2021 SPIE and IS&T [DOI: 10.1117/***.30.3.033004] Under the influence of severe weather, the quality of images collected by the camera system drops sharply. These degraded images not only affect people's subjective judgment but also lead to poor results on advanced computer vision tasks such as object detection,1 semantic segmentation,2 action recognition,3 and so on. The restoration of images in severe weather4 is of great significance to computer vision. Image dehazing algorithms are designed to recover high-quality clear images from low-quality hazy images for advanced computer vision tasks. The image dehazing task assists in the driving technology of unmanned vehicles and the unmanned toll sys

关键词： image dehazing dense feedback recurrent iteration encoder-decoder

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Detailed feature extraction network-based fine-grained face segmentation

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KNOWLEDGE-BASED SYSTEMS 2022年第0期250卷

作者： Umirzakova, Sabina Whangbo, Taeg Keun Gachon Univ Dept IT Convergence Engn Seongnam South Korea Gachon Univ Dept Comp Sci Seongnam South Korea

Face parsing refers to the labeling of each facial component in a face image and has been employed in facial stimulation, expression recognition, and makeup use, effectively providing a basis for further analysis, computations, animation, modification, and numerous other applications. Although existing face parsing methods have demonstrated good performance, they fail to extract rich features and recover accurate segmentation maps, particularly for faces with high variations in expression and sufficiently similar appearances. Moreover, these approaches neglect the semantic gaps and dependencies between facial categories and their boundaries. To address these drawbacks, we propose an efficient dilated convolution network with different aspect ratios to attain accurate face parsing of the output by applying the feature extraction capability. The proposed network-structured multiscale dilated encoder-decoder convolution model obtains rich component information and efficiently improves the capture of global information by obtaining low- and high-level semantic features. To achieve a delicate parsing output of the face components along the borders and analyze the connections between the face categories and their border edges, the semantic edge map is learned using a conditional random field, which aims to distinguish border and non-border pixels during the modeling. We conducted experiments using three well-known publicly available face databases. The recorded results demonstrate the high accuracy and capacity of the proposed method in comparison to previous state-of-art methods. Our proposed model achieved a mean accuracy of 90% on the CelebAMask-HQdataset for the category case and 81.43% for the accessory case, and achieved accuracies of 91.58% and 92.44% on the HELEN and LaPa datasets, respectively, thereby demonstrating its effectiveness. (C) 2022 The Author(s). Published by Elsevier B.V.

关键词： Face segmentation Dilated convolution Multiscale encoder-decoder Conditional random field

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An unsupervised deep global-local views model for anomaly detection in attributed networks

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KNOWLEDGE-BASED SYSTEMS 2024年 300卷

作者： Lei, Tianyang Ou, Mengxin Gong, Chang Li, Jichao Yang, Kewei Natl Univ Def Technol Coll Syst Engn Deya Rd 109 Changsha 410073 Peoples R China

An attribute network is a form of data that contains rich semantic information. Many real scenarios can be modeled as attributed networks, such as social media, citations, and traffic networks. Anomaly detection in attributed networks is an interesting research topic owing to its potential in various practical applications, including spam, network intrusion, and financial fraud detection. However, attributed networks exhibit many anomaly patterns, such as structural, attribute, local, and global anomalies, making anomaly detection in attributed networks a challenging task. To address these difficulties, we designed DeepGL, a novel unsupervised deep global-local view model, for anomaly detection in attributed networks. Our model is an encoder-decoder framework with multiple views that capture node attributes and network structure information from both global and local views. Specifically, our model contains two encoders and four decoders. The two encoders are used to capture network features from local and global views, and the four decoders are used to reconstruct the local node attribute information, local structure information, global node attribute information, and global structure information. To the encoders and decoders, we applied Laplacian sharpening and smoothing techniques to maintain the integrity of normal node features while diminishing the conspicuousness of anomalous nodes in the reconstructed information, thereby facilitating the calculation of reconstruction errors. Extensive experiments on four real-world attributed network datasets demonstrate the excellent performance of the proposed method.

关键词： Anomaly detection Attributed network Graph convolutional networks encoder-decoder

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Dual-adaptive interactive transformer with textual and visual context for image captioning

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 243卷

作者： Chen, Lizhi Li, Kesen Soochow Univ Sch Software Suzhou 215000 Peoples R China Zhejiang A&F Univ Coll Engn Technol Jiyang Coll Zhuji 311899 Peoples R China

The multimodal Transformer, which integrates visual and textual contextual information, has recently shown success in image captioning tasks. However, there is still natural complementarity and redundancy between text and vision, and effectively integrating the information from both modalities is crucial for comprehending the content of an image. In this paper, we propose the Dual-Adaptive Interactive Transformer (DAIT), which incorporates similar textual and visual contextual information into the encoding and decoding stages. Specifically, during encoding, we propose the Adaptive Interactive encoder (AIE), which expands the feature vectors for both modalities through the introduction of new operations. Furthermore, we also introduce normalization gate factors to mitigate noise caused by the interaction between the two modalities. During decoding, we propose the Adaptive Interactive decoder (AID), which adaptively adjusts the multimodal features at each moment through similarity-weighted textual and visual branches. To validate our model, we conducted extensive experiments on the MS COCO image captioning dataset and achieved outstanding performance compared to many state-of-theart methods.

关键词： Image captioning Transformer Textual and visual encoder-decoder Adaptive Interactive

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Precursors-driven machine learning prediction of chaotic extreme pulses in Kerr resonators

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CHAOS SOLITONS & FRACTALS 2022年 160卷

作者： Coulibaly, Saliya Bessin, Florent Clerc, Marcel G. Mussot, Arnaud Univ Lille CNRS UMR 8523 PhLAM Phys Lasers Atomes & Mol F-59000 Lille France Aston Univ Aston Inst Photon Technol Birmingham B4 7ET England Univ Chile Dept Fis FCFM Casilla 487-3 Santiago Chile Univ Chile Millennium Inst Res Opt FCFM Casilla 487-3 Santiago Chile

Machine learning algorithms have opened a breach in the prediction's fortress of high-dimensional chaotic systems. Their ability to find hidden correlations in data can be exploited to perform model-free forecasting of spatiotemporal chaos and extreme events. However, the extensive feature of these evolutions makes up a critical limitation for full-size forecasting processes. Hence, the main challenge for forecasting relevant events is to establish the set of pertinent information. Here, we identify precursors from the transfer entropy of the system and a deep Long Short-Term Memory network to forecast the complex dynamics of a system evolving in a high dimensional spatiotemporal chaotic regime. Performances of this triggerable model-free prediction protocol based on the information flowing map are tested from experimental data originating from a passive resonator operating in such a complex nonlinear regime. We have been able to predict the occurrence of extreme events up to 9 round trips after the detection of precursor, i.e., 3 times the horizon provided by Lyapunov exponents, with 92% of true positive predictions leading to 60% of *** have implemented a process to forecast extreme events in a fully developed turbulent flow. The novelty of our strategy lies in the information's use theory method to detect precursors and use them as the input of a neural network to infer the incoming extreme events. Our process is suitable for all the extended dissipative systems that can only be partially observed or real-world data.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Spatiotemporal Chaos Kerr frequency combs Forecasting extreme events Machine learning Long Short-Term Memory (LSTM) encoder-decoder Transfer entropy

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Modeling social interaction and intention for pedestrian trajectory prediction

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2021年 570卷 125790-125790页

作者： Chen, Kai Song, Xiao Ren, Xiaoxiang Beihang Univ BUAA Beijing Peoples R China Nanan Primary Sch Nanan Shanxi Peoples R China

Future pedestrian trajectory prediction offers great prospects for many practical applications. Most existing methods focus on social interaction among pedestrians but ignore the fact that in addition to pedestrians there are other kinds of objects (cars, dogs, bicycles, motorcycles, etc.) with a great influence on the subject pedestrian's future trajectory. Most existing methods neglect the intentions of the pedestrian, which can be obtained by the key points of the subject pedestrian's face. Therefore, rich category information about the subject pedestrian's surroundings and face key points plays a great role in promoting the modeling of pedestrian movement. Motivated by this idea, this paper tries to predict a pedestrian's future trajectory by jointly using various categories and the relative positions of the subject pedestrian's surroundings and the key points in his face. We propose a data modeling method to effectively unify rich visual features about categories, interaction and face key points into a multi-channel tensor and build an end-to-end fully convolutional encoder-decoder attention model based on convolutional long-short-term memory utilizing this tensor. We evaluate and compare our method with several existing methods on 5 crowded video sequences from the public dataset multi-object tracking (MOT)-16. Experimental results show that our method outperforms state-of-the-art approaches, with less prediction error. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Social-interaction Pedestrian intention Convolutional long-short-term memory encoder-decoder Attention

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Learning from what we know: How to perform vulnerability prediction using noisy historical data

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EMPIRICAL SOFTWARE ENGINEERING 2022年第7期27卷 169-169页

作者： Garg, Aayush Degiovanni, Renzo Jimenez, Matthieu Cordy, Maxime Papadakis, Mike Le Traon, Yves Univ Luxembourg Dept Comp Sci Fac Sci Technol & Med FSTM Esch Sur Alzette Luxembourg Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SnT Esch Sur Alzette Luxembourg Univ Luxembourg Fac Sci Technol & Med Esch Sur Alzette Luxembourg Univ Luxembourg Esch Sur Alzette Luxembourg

Vulnerability prediction refers to the problem of identifying system components that are most likely to be vulnerable. Typically, this problem is tackled by training binary classifiers on historical data. Unfortunately, recent research has shown that such approaches underperform due to the following two reasons: a) the imbalanced nature of the problem, and b) the inherently noisy historical data, i.e., most vulnerabilities are discovered much later than they are introduced. This misleads classifiers as they learn to recognize actual vulnerable components as non-vulnerable. To tackle these issues, we propose TROVON, a technique that learns from known vulnerable components rather than from vulnerable and non-vulnerable components, as typically performed. We perform this by contrasting the known vulnerable, and their respective fixed components. This way, TROVON manages to learn from the things we know, i.e., vulnerabilities, hence reducing the effects of noisy and unbalanced data. We evaluate TROVON by comparing it with existing techniques on three security-critical open source systems, i.e., Linux Kernel, OpenSSL, and Wireshark, with historical vulnerabilities that have been reported in the National Vulnerability Database (NVD). Our evaluation demonstrates that the prediction capability of TROVON significantly outperforms existing vulnerability prediction techniques such as Software Metrics, Imports, Function Calls, Text Mining, Devign, LSTM, and LSTM-RF with an improvement of 40.84% in Matthews Correlation Coefficient (MCC) score under Clean Training Data Settings, and an improvement of 35.52% under Realistic Training Data Settings.

关键词： Vulnerability prediction Trovon Training on vulnerabilities only encoder-decoder Machine translation tf-seq2seq

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A Multi-Level Attention Model for Remote Sensing Image Captions

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REMOTE SENSING 2020年第6期12卷 939-939页

作者： Li, Yangyang Fang, Shuangkang Jiao, Licheng Liu, Ruijiao Shang, Ronghua Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist EducSch Artificial Intelligence Int Res Ctr Intelligent Percept & ComputatJoint Xian 710071 Peoples R China

The task of image captioning involves the generation of a sentence that can describe an image appropriately, which is the intersection of computer vision and natural language. Although the research on remote sensing image captions has just started, it has great significance. The attention mechanism is inspired by the way humans think, which is widely used in remote sensing image caption tasks. However, the attention mechanism currently used in this task is mainly aimed at images, which is too simple to express such a complex task well. Therefore, in this paper, we propose a multi-level attention model, which is a closer imitation of attention mechanisms of human beings. This model contains three attention structures, which represent the attention to different areas of the image, the attention to different words, and the attention to vision and semantics. Experiments show that our model has achieved better results than before, which is currently state-of-the-art. In addition, the existing datasets for remote sensing image captioning contain a large number of errors. Therefore, in this paper, a lot of work has been done to modify the existing datasets in order to promote the research of remote sensing image captioning.

关键词： remote sensing image image caption attention encoder-decoder

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DCNet: A Deformable Convolutional Cloud Detection Network for Remote Sensing Imagery

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Liu, Yang Wang, Wen Li, Qingyong Min, Min Yao, Zhigang Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China Sun Yat Sen Univ Sch Atmospher Sci Zhuhai 519082 Peoples R China Beijing Inst Appl Meteorol Beijing 100029 Peoples R China

Recently, deep convolutional neural networks (CNNs) have made important progress in cloud detection with powerful representation learning capability and yield significant performance. However, most existing CNN-based cloud detection methods still face serious challenges because of the variable geometry of clouds and the complexity of underlying surfaces. It is attributed that they only use the fixed grid to extract contextual information, which lacks internal mechanisms to handle the geometric transformations of clouds. To tackle this problem, we propose a deformable convolutional cloud detection network with an encoder-decoder architecture, named DCNet, which can enhance the adaptability of a model to cloud variations. Specifically, we introduce deformable convolution blocks at the encoder to capture saliency spatial contexts adaptively based on the morphological characteristics of clouds and generate high-level semantic representations. After this, we incorporate skip-connection mechanisms into the decoder that integrate low-level spatial contexts as guidance to recover high-level semantic pixel localization and export precise cloud-detection results. Extensive experiments on the GF-1 wide field-of-view (WFV) Satellite Imagery demonstrate that DCNet outperforms several state-of-the-art methods. A public reference implementation of our proposed model in PyTorch is available at shapehttps://***/NiAn-creator/***.

关键词： Clouds Convolution Feature extraction Semantics Satellites Decoding Task analysis Cloud detection deformable convolution block (DCB) encoder-decoder semantic segmentation

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Data-Driven Voltage Prognostic for Solid Oxide Fuel Cell System Based on Deep Learning

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ENERGIES 2022年第17期15卷 6294页

作者： Li, Mingfei Wu, Jiajian Chen, Zhengpeng Dong, Jiangbo Peng, Zhiping Xiong, Kai Rao, Mumin Chen, Chuangting Li, Xi Sci & Technol Res Inst Co Ltd Guangdong Energy Grp Guangzhou 511466 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Imaging Proc & Intelligent Control Educ Minist Wuhan 430074 Peoples R China Guangdong Huizhou Lng Power Co Ltd Huizhou 516081 Peoples R China Guangdong Energy Grp Co Ltd Guangzhou 510630 Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518055 Peoples R China

A solid oxide fuel cell (SOFC) is an innovative power generation system that is green, efficient, and promising for a wide range of applications. The prediction and evaluation of the operation state of a solid oxide fuel cell system is of great significance for the stable and long-term operation of the power generation system. Prognostics and Health Management (PHM) technology is widely used to perform preventive and predictive maintenance on equipment. Unlike prediction based on the SOFC mechanistic model, the combination of PHM and deep learning has shown wide application prospects. Therefore, this study first obtains an experimental dataset through short-term degradation experiments of a 1 kW SOFC system, and then proposes an encoder-decoder RNN-based SOFC state prediction model. Based on the experimental dataset, the model can accurately predict the voltage variation of the SOFC system. The prediction results of the four different prediction models developed are compared and analyzed, namely, long short-term memory (LSTM), gated recurrent unit (GRU), encoder-decoder LSTM, and encoder-decoder GRU. The results show that for the SOFC test set, the mean square error of encoder-decoder LSTM and encoder-decoder GRU are 0.015121 and 0.014966, respectively, whereas the corresponding error results of LSTM and GRU are 0.017050 and 0.017456, respectively. The encoder-decoder RNN model displays high prediction precision, which proves that it can improve the accuracy of prediction, which is expected to be combined with control strategies and further help the implementation of PHM in fuel cells.

关键词： solid oxide fuel cell recurrent neural network long short-term memory gated recurrent unit encoder-decoder state prediction

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